Vector Databases & Vector Search with Qdrant
Master the power of Vector Search and Retrieval-Augmented Generation (RAG) using Qdrant! In this course, you’ll learn how to build high-performance vector databases, index high-dimensional embeddings, and implement efficient semantic search for modern AI applications. From core indexing concepts to seamless LLM knowledge integration, unlock the exact tools and workflows needed to scale production-grade RAG pipelines.
Course Topics
This is a snapshot of what's covered. Contact us for the complete module-by-module curriculum, batch schedule, and pricing.
Skills covered
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Corporate / Group Training- Live + recorded sessions with lifetime access
- Hands-on labs & real projects
- Certificate of completion
- Mobile learning & downloadable resources
- Instructor Q&A & discussion forums
About this course
This hands-on course equips software engineers, data scientists, and AI practitioners with the expertise needed to design, build, and evaluate production-ready vector search systems using Qdrant—one of the fastest and most scalable vector engines built in Rust.
What you'll learn
- Master dense, sparse, and hybrid vector search techniques for high-accuracy retrieval
- Understand HNSW indexing mechanics, scalar quantization, and product quantization in Qdrant
- Architect scalable multi-tenant RAG systems with advanced payload filtering and payload indexing
- Implement state-of-the-art reranking models using Cross-Encoders and Cohere Rerank
- Evaluate retrieval quality using metrics like NDCG, MRR, and Precision@K
- Integrate Qdrant seamlessly into production Python AI and backend application pipelines
Program Roadmap
Vector Embeddings
Vector Embeddings are numerical representations of real-world data—such as text, images, audio, or video—converted into long lists of numbers (vectors) in a multidimensional space.
Qdrant
Instead of searching for exact keyword matches, vector embeddings allow AI models and databases to capture the semantic meaning and context of data.
Retrieval-Augmented Generation (RAG)
Vector Embeddings are numerical representations of real-world data—such as text, images, audio, or video—converted into long lists of numbers (vectors) in a multidimensional space.
